Variability of the prevalence of depression in function of sociodemographic and environmental factors: Ecological model
Entity
UAM. Departamento de Economía AplicadaPublisher
Frontiers Media S.A.Date
2018-11-12Citation
10.3389/fpsyg.2018.02182
Frontiers in Psychology 9.November (2018): Article 2182
ISSN
1664-1078DOI
10.3389/fpsyg.2018.02182Funded by
This work was supported by the Health Institute Carlos III (grant number Medical research Council (grant number PS09/01378); Research Group B21_R17 of the Department of Research, Innovation and University of the Government of Aragon (Spain); and Feder Funds “Another way to make Europe”.Project
Gobierno de España. PS09/01378Editor's Version
https://doi.org/10.3389/fpsyg.2018.02182Subjects
Depression; Environmental factors; Prevalence; Sociodemographic factors; Variability; Ciencias SocialesRights
© 2018 Llorente, Oliván-Blázquez, Zuñiga-Antón, Masluk, Andrés, Garcia-Campayo and Magallón-Botaya.Abstract
Major depression etiopathogenesis is related to a wide variety of genetics, demographic and psychosocial factors, as well as to environmental factors. The objective of this study is to analyze sociodemographic and environmental variables that are related to the prevalence of depression through correlation analysis and to develop a regression model that explains the behavior of this disease from an ecological perspective. This is an ecological, retrospective, cross-sectional study. The target population was 1,148,430 individuals over the age of 16 who were registered in Aragon (Spain) during 2010, with electronic medical records in the community's primary health care centers. The spatial unit was the Basic Health Area (BHA). The dependent variable was the diagnosis of Depression and the ecological independent variables were: Demographic variables (gender and age), population distribution, typology of the entity, population structure by sex and age, by nationality, by education, by work, by salary, by marital status, structure of the household by number of members, and state of the buildings. The results show moderate and positive correlations with higher rates of depression in areas having a higher femininity index, higher population density, areas with a higher unemployment rate and higher average salary. The results of the linear regression show that aging +75 and rural entities act as protective factors for depression, while urban areas and deficient buildings act as risk factors. In conclusion, the ecological methodology may be a useful tool which, together with the statistical epidemiological analysis, can help in the political decision making process.
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Google Scholar:Llorente, José María
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Oliván-Blázquez, Bárbara
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Zuñiga-Antón, María
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Masluk, Bárbara
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Andrés, Eva
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García-Campayo, Javier
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Magallón-Botaya, Rosa
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